There is a need to automatically and accurately track the amount of time a person or object spends interacting or associating with other people or objects. This association information may be used for accounting purposes, for worker payroll, to bill a customer, or to log the “work expended” on a given object or by a given person. Alternatively, the information may be used for inventory records, equipment utilization studies, event precipitation and similar uses. Unfortunately, the accuracy of today's object association systems is inadequate. Conventional object association systems require estimates to capture the amount of time devices spend interacting. For example, it is quite common to estimate the amount of time that an expensive piece of medical equipment was used during a procedure. Since medical equipment can generate millions of dollars a year in bills corresponding to the time the equipment is operated, a small inaccuracy in estimation of the time of operation has a big impact on either the payer or the payee. Accordingly, it is becoming more common for medical insurance companies to demand exact time recordings of the usage of particular equipment. Since this requires human oversight, the process becomes overly burdensome for the medical staff.
The need for humans to initiate conventional object association systems represents a major difficulty with the systems. This requirement for manual interaction, typically to start and stop timers or record times, results in inaccurate readings that can be subject to fraud. Some people simply forget to start or stop the timers, especially when they have multiple tasks to perform, or they just estimate the time to keep things simple. In most cases they do not stop the timers when they take small breaks, which further leads to inaccurate readings. In some cases, people start or stop the time tracking system fraudulently which results in inaccurate billing. Additionally, accurately tracking the time that objects spend interacting is difficult since the objects, absent an interface with a timer, can not start a timer. A person typically needs to be involved in some way. Unfortunately, conventional association systems are not designed to determine and log associations automatically without human intervention.
Conventional object association systems also fail to track multiple tasks, either sequentially or simultaneously. In “time clock” type systems, if there are multiple objects or tasks to be tracked, multiple timers are typically used. These timers can track when a human operator notes that two devices begin to interact, but the problem rapidly becomes too complex to record if there are multiple devices interacting with other devices. Conventional wireless tether systems are limited to noting when two devices are close to each other. They are typically not equipped to handle multiple object interactions where starting and stopping is involved. The location system solutions simply show that multiple devices are in the same space. They do not show which object is interacting with another nor the times of these interactions as they have difficulty in determining interaction detail. Additionally, most current systems do not have the ability to automatically and to continuously track object interactions, such as tracking the progress of a piece of work in process (WIP) and the time it spends interacting with various tools and people, in order to make that information available in “real time” to an interested party. Without this ability to review real-time object association data, supervisors or systems have difficulty in quickly recognizing problems in a production flow.
Sites where location systems are used, such as hospitals, may be large and complex. This can make tracking resources within the site, such as a hospital, a complicated task. As such, it may be useful to subdivide the site into locales of interest. As used herein, the term locale is intended to include any area, site, location or point of interest. For example, hospitals have several types of specialty purpose rooms such as patient rooms, emergency rooms, operating rooms, intensive care rooms, quarantine rooms, laboratories, equipment rooms, etc. Each of these rooms can constitute a locale within the hospital. Indeed, such locales are typically the level of granularity for locations that hospitals typically work with. For example, patients are assigned to rooms, samples are sent to laboratories, and doctors schedule the use of operating rooms. Associating an object or person with such a locale provides a convenient level of granularity for tracking resources as well as providing context for the calculated location of the object or person. For example, knowing a doctor is in an operating room may be more useful than knowing that the doctor is at coordinates X, Y, Z.
In many instances, an interaction between an object and another object may be inconsequential. For example, a doctor may pass within a close proximity of a patient on the way to treat another patient. If criteria for association were based solely on proximity, such passing proximity could be determined to be an association between the doctor and the patient even though the doctor had no actual interaction with the patient. Likewise, the limitations of the hardware used to determine location may cause the location of an object or person to briefly change or to show inconsistent location. For example, a doctor may be in a first locale, such as a room, that is directly adjacent to a second locale, such as another room. If the doctor is against a wall in the first room that is adjacent to the second room, it is possible that the calculated location of the doctor may show that the doctor is suddenly in the second room and then back in the first room even though the doctor never actually changed rooms. In both of these examples, the interaction of the doctor with a patient or locale was too brief for an actual interaction to occur, and therefore an association between the doctor and the patient or locale should be not formed.